Human-in-the-loop design patterns for AI workflows — companion videos

AI Engineer

Dex Horthy explains why reliable agent systems are mostly disciplined software around a few LLM calls: own the prompt, own the context window, keep control flow deterministic and use tool calls to contact humans when the workflow needs judgment. That maps directly to the article's approval, exception and escalation patterns.

What you should get from this: Learn how to design AI workflows that can pause, resume, ask for human judgment and keep business state separate from model guesses.

Watch or know first: Basic familiarity with tool-calling or multi-step AI workflows.

AWS Events

This lightning talk names the business moments where human control is needed: high-stakes decisions, irreversible actions, regulatory requirements, trust-building phases, ambiguous edge cases and graceful degradation. It also shows concrete implementation mechanisms such as MCP elicitations, Step Functions callback waits and approval nodes.

What you should get from this: See how approval gates can be implemented as explicit workflow checkpoints rather than informal manual review after something goes wrong.

Watch or know first: Some experience with event-driven workflows, cloud orchestration or agent-tool integration.